The successful treatment of cancer is dependent upon an accurate diagnosis of the tumor. It has become clear that while many tumors appear indistinguishable at the morphological level, they are in fact molecularly distinct, and such molecular distinctions can be predictive of clinical outcome. The present research proposal lays out a strategy for developing a molecular classification system for two of the most common human tumors: adenocarcinoma of the lung and prostate. The classification system will be based upon gene expression profiles obtained using DNA microarray technologies. There are three phases to the proposed project: 1) gene expression data collection for 42,000 genes and ESTs using oligonucleotide arrays for a series of lung and prostate adenocarcinoma patients with known clinical outcome, 2) classification model building using both supervised and unsupervised learning techniques, and 3) testing of the validity of these models on an independent set of lung and prostate adenocarcinoma samples. It is hoped that the development of a molecular classification system for these common tumors will help to optimize the use of existing anti-cancer therapies, and may also lay the groundwork for the development of new therapeutic strategies targeted to patients with particular subsets of these diseases.